Comparisons
Chroma vs KDB.AI

Chroma vs. KDB.AI

Compare Chroma vs. KDB.AI by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.

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Chroma vs. KDB.AI on Scalability

ChromaKDB.AI

Separation of storage and compute

❌

Separation of query and insertions

No. Can not scale beyond single node.

Yes.

Multi-replication

❌

Dynamic segment placement vs. static data sharding

No distributed data replacement

Neither.

Cloud-native

❌

Billion-scale vector support

❌

Chroma scalability

Without any distributed data replacement, Chroma is not able to scale beyond a single node

KDB.AI

KDB.AI is a scalable vector database.

Chroma vs. KDB.AI on Functionality

Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.

ChromaKDB.AI

Role-based Access Control (RBAC)

❌

Disk Index support

❌

Yes (qFlat and qHNSW)

Hybrid Search (ie Scalar filtering)

Yes with scalar filtering

Yes. Hybrid Sparse & Dense Search

Partitions/namespaces/logical groups

❌

Index type supported

1 (HNSW)

Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.

Multiple in-memory indexes support

❌

Chroma functionality

Chroma uses HNSW algorithm to support kNN search.

KDB.AI

Built by KX, a database provider known for time-series data management, KDB.AI enables developers to bring temporal and semantic context and relevancy to their applications. It supports various search types, including vector similarity search, hybrid sparse and vector search, and Non-Transformed TSS, a similarity search algorithm specific for time series data. It uses Cosine Similarity, Inner Product, and L2 Distance (Euclidean) for similarity metrics.

Chroma vs. KDB.AI on Purpose-built

What’s your vector database for?

A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:

Scalability and tunability
Multi-tenancy and data isolation
A complete suite of APIs
An intuitive user interface/administrative console

ChromaKDB.AI

Purpose-built for Vectors

✔️

Yes.

Tunable consistency

❌

Support for both stream and batch of vector data

❌

Binary Vector support

❌

Multi-language SDK

Python, JavaScript

Python

Database rollback

❌

Chroma vs. KDB.AI: what’s right for me?

Chroma

Chroma is maintained by a single commercial company offering a non-scalable single node. License: Apache-2.0 license

KDB.AI

KDB.AI is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications using real-time data.

Proprietary license